Compliance management for emerging risks

ABSTRACT

Disclosed herein are system, method, and computer program product embodiments for identifying and managing emerging risks. Businesses, such as those participating in regulated industries, may need to regularly monitor enforcement actions that are cited in a variety of sources. Embodiments disclosed herein provide for scraping information from those sources and comparing it to existing risk events in order to determine possible discrepancies that should be assessed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/536,678, entitled “COMPLIANCE MANAGEMENT FOR EMERGING RISKS,” filedAug. 9, 2019, which is incorporated by reference herein in its entirety.

BACKGROUND

For many businesses, identifying and dealing with customer complaints isa priority. This includes identifying these complaints on third partywebsites and addressing them before they cause reputational damage tothe business.

However, in some lines of business, the potential issues may be largerthan reputational—regulatory risk exposure may be involved, for example.Given the number of possible sources of risk, approaches are needed tobetter understand and manage the businesses exposure to these risks.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated herein and form a part of thespecification.

FIG. 1 illustrates a risk assessment process flow, in accordance with anembodiment.

FIG. 2 illustrates a scraping module, in accordance with an embodiment.

FIG. 3 is a flowchart illustrating steps by which risks are identified,in accordance with an embodiment.

FIG. 4 is a flowchart illustrating steps by which a notification servicemay provide subscribers with relevant risk event information, inaccordance with an embodiment.

FIG. 5 is an example computer system useful for implementing variousembodiments.

In the drawings, like reference numbers generally indicate identical orsimilar elements. Additionally, generally, the left-most digit(s) of areference number identifies the drawing in which the reference numberfirst appears.

DETAILED DESCRIPTION

Provided herein are system, apparatus, device, method and/or computerprogram product embodiments, and/or combinations and sub-combinationsthereof, for identifying and managing emerging risks.

Businesses in many industries need to deal with emerging risks ofpossible enforcement actions. For example, businesses in theheavily-regulated banking industry must be aware of possible regulatoryviolations they have committed. And, beyond being aware of thesepossible violations, they must properly weigh the risks of theseviolations in terms of how they should be addressed.

Presently, businesses handle such tasks mostly manually. For example,such businesses may manually log possible regulatory violations forfollow up. Separately, such businesses may subscribe to variouspublications that provide updates on regulatory enforcement actionsrelevant to the operation of those businesses. News publications,aggregators, and government websites may supply updates on the variousenforcement actions, and need to either be visited manually or digestsof updates reviewed manually. Not only is this process time consuming,but also prone to error—typically a failure to identify and properlyappraise risk.

FIG. 1 illustrates a risk assessment process flow 100, in accordancewith an embodiment. Flow 100 incorporates various elements that may beexecuted in a variety of contexts, including locally or remotely to acommon system, as well as within a cloud-based architecture. A skilledartisan will appreciate that the precise architecture of a systemimplementing flow 100 will therefore vary depending on where theelements of flow 100 are executed.

Scraping module 102 provides access to websites and other documentsources of enforcement information, in accordance with an embodiment.Scraping module 102 accesses these document sources and obtains keydetails regarding these various documents. By way of non-limitingexample, these details may include a regulatory citation to which anenforcement action described in the document relates.

Comparator module 104 uses these scraped details and compares them tosources of risk information to determine whether risks are present, inaccordance with an embodiment. By way of non-limiting example,comparator module 104 uses the regulatory citation details from adocument scraped by scraping module 102 to compare with regulatorycitations associated with a risk.

In accordance with an embodiment, sources of risk information mayinclude complaints data 106 and risk event data 108. Complaints data 106may include complaints made directly to a business operating processflow 100, or on a separate platform and obtained by a scraping mechanismlike (or including) scraping module 102. Risk event data 108 includesknown risk events, such as possible regulatory violations that thebusiness may have incurred—but perhaps has not yet had an enforcementaction brought against it for the same. These risk events of risk eventdata 108 may be entered a variety of ways, including through manualentry.

As noted above, comparator module 104 compares the scraped details fromscraping module 102 with risk information, such as risk information fromcomplaints data 106 and risk event data 108. In accordance with anembodiment, comparator module 104 matches an identified regulatorycitation from a scraped document identified by scraping module 102 to aregulatory citation of risk information from complaints data 106 or riskevent data 108.

However regulatory citation information may not be immediately availableand associated with either the scraped documents or the riskinformation. In accordance with an embodiment, the scraped documents orthe risk information may be searched for regulatory citations.Regulatory citations typically have a regular form, and can beidentified through the use of, by way of non-limiting example,regulatory expressions.

In accordance with an embodiment, comparator 104 may compare scrapeddocuments with risk information on the basis of classificationinformation. This classification information may, in accordance with afurther embodiment, relate to a specific regulatory citation, although askilled artisan will appreciate that other classifications may be used.Additionally, the classification information may form a basis forcomparing with a regulatory citation. For example, if the scrapeddocument contains a regulatory citation, but the risk information doesnot, a classification for the risk information may suggest acorresponding regulatory citation for that classification that can beused for comparison purposes.

Classification of documents, including scraped documents provided byscraping module 102 and risk information from complaints data 106 orrisk event data 108, may be performed through a variety of mechanisms,including keyword-based classification, or manual classification.However, in accordance with an embodiment, classification is performedin accordance with document classification approaches discussed inco-pending U.S. patent application Ser. No. 16/536,645, entitled “SEARCHPATTERN SUGGESTIONS FOR LARGE DATASETS,” filed Aug. 9, 2019, andincorporated herein by reference in its entirety. For example,phrase-based scoring may be employed against a full set of documents inorder to determine a classification for the scraped documents and riskinformation.

Comparator module 104 may then store this relationship between scrapeddocuments and risk information in comparisons database 110, inaccordance with an embodiment. In accordance with an embodiment, thisrelationship is termed a discrepancy.

In accordance with a further embodiment, comparator module 104 mayperform a comparison of complaints data 106 directly to risk event data108. This comparison may proceed again in accordance with the aboveclassification approach, and the results of this comparison would againbe stored as discrepancies in comparisons database 110.

Events manager 112 is an exemplary component configured to access thediscrepancies stored in comparisons database 110 in order to facilitateassessment of risks presented by the discrepancies, in accordance withan embodiment. Event manager 112 may provide access to comparisonsdatabase 110 to a risk analysis module 114, in accordance with anembodiment. Access to risk analysis module 114 may be provided by way ofa user interface (e.g., a graphical user interface (GUI)) accessible toa risk analyst. The user interface may present the discrepancies to therisk analyst for the purpose of prioritizing a risk level for eachdiscrepancy, further classification of the discrepancies, or otherwiseinteracting with the discrepancies. A skilled artisan will appreciatethat any manner of visualizing and interacting with discrepancies storedin comparisons database 110 is contemplated within the scope of thisdisclosure.

Additionally, a notification service 116 may access discrepancies fromevents manager 112 for the purpose of notifying relevant actors ofrelevant discrepancies. For example, as shown in flow 100, various linesof business 118 a-118 n (collectively, lines of business 118) may beinterested in discrepancies as they are added to comparisons database110. These lines of business 118 may register with notification service116 in order to receive updates of the relevant discrepancies.

Relevance of the discrepancies may be determined, by way of non-limitingexample, on the basis of the classification associated with theunderlying documents and risk information for a discrepancy. Aspreviously noted, this classification may be determined as described inco-pending U.S. patent application Ser. No. 16/536,645, entitled “SEARCHPATTERN SUGGESTIONS FOR LARGE DATASETS,” filed Aug. 9, 2019, filedconcurrently herewith and incorporated herein by reference in itsentirety. Additionally, relevance of the discrepancies may be determinedon the basis of other information used for performing comparisons bycomparator 104, such as regulatory citations as described above.

FIG. 2 illustrates a scraping module 200, in accordance with anembodiment. By way of non-limiting example, scraping module 200 may beemployed as scraping module 102 of flow 100 of FIG. 1. Scraping module200 is used to provide scraped information 212 from various sources,such as public websites 206, social media platforms 208, and governmentagency forums 210, by way of a non-limiting example.

Each source of information may be arranged in a variety of differentways, and may not itself provide an interface for directly accessingrelevant information to be accessed. Accordingly, a scraping interface204 may be provided, in accordance with an embodiment. In an embodiment,scraping interface 204 provides the ability to read text informationfrom sources, such as sources 206, 208, and 210. By way of non-limitingexample, scraping interface 204 may look for fields within a markuplanguage document that contain relevant text. By way of furthernon-limiting example, scraping interface 204 may include opticalcharacter recognition (OCR) capabilities for obtaining text from scanneddocuments, such as portable document format (PDF) documents.

Scraping interface 204 provides the scraped text to scraping engine 202,in accordance with an embodiment. Scraping engine 202 may performscraping tasks in order to extract relevant information from the scrapedtext. For example, scraping engine 202 may identify regulatory citationswithin the scraped text. In another example, scraping engine 202 mayperform classification of the scraped text. And in yet another example,scraping engine 202 may summarize the scraped text using phraseextraction. Classification and phrase extraction of the scraped text maybe performed as described in co-pending U.S. patent application Ser. No.16/536,645, entitled “SEARCH PATTERN SUGGESTIONS FOR LARGE DATASETS,”filed Aug. 9, 2019, filed concurrently herewith and incorporated hereinby reference in its entirety.

The results of scraping engine 202, such as classification information,extracted phrases, regulatory citations within the scraped text, and anyother relevant information obtained, may then be stored as scrapedinformation 212, such as in a database, in accordance with anembodiment. Scraped information 212 may then be accessed by othermodules, such as comparator 104 of FIG. 1.

FIG. 3 is a flowchart 300 illustrating steps by which risks areidentified, in accordance with an embodiment. The process begins at step302 where enforcement or complaints information is received. Referringagain to FIG. 1, enforcement or complaints information is received atcomparator 104 from either scraping module 102 or complaints data 106,in accordance with an embodiment.

At step 304, this enforcement or complaints information is comparedagainst existing risk events using citation data, in accordance with anembodiment. In accordance with a further embodiment, this citation datais determined based on classification of the enforcement or complaintsinformation and of the existing risk events. By way of non-limitingexample, this comparison is performed by comparator 104 of FIG. 1.

At step 306, the results of this comparison are stored as discrepanciesfor risk analysis purpose, and access is provided in order to performthis analysis. By way of non-limiting example, the discrepancies arestored by comparator 104 of FIG. 1 in comparisons database 110 of FIG.1, and access is provided to events manager 112 of FIG. 1.

FIG. 4 is a flowchart 400 illustrating steps by which a notificationservice may provide subscribers with relevant risk event information, inaccordance with an embodiment. The process begins at step 402 where newcomparison information is received in an event. By way of non-limitingexample, this event is a discrepancy that has been added to acomparisons database, such as comparisons database 110 of FIG. 1 andidentified as a new addition by an events manager, such as eventsmanager 112 of FIG. 1.

The process continues to step 404 where a relevant line of business isidentified for the event, and at step 406 a notification is pushed tothe relevant line of business, in accordance with an embodiment. By wayof non-limiting example, a relevant line of business may be determinedbased on a classification or a regulatory citation of the discrepancy asdescribed above.

Various embodiments may be implemented, for example, using one or morewell-known computer systems, such as computer system 500 shown in FIG.5. One or more computer systems 500 may be used, for example, toimplement any of the embodiments discussed herein, as well ascombinations and sub-combinations thereof.

Computer system 500 may include one or more processors (also calledcentral processing units, or CPUs), such as a processor 504. Processor504 may be connected to a communication infrastructure or bus 506.

Computer system 500 may also include user input/output device(s) 503,such as monitors, keyboards, pointing devices, etc., which maycommunicate with communication infrastructure 506 through userinput/output interface(s) 502.

One or more of processors 504 may be a graphics processing unit (GPU).In an embodiment, a GPU may be a processor that is a specializedelectronic circuit designed to process mathematically intensiveapplications. The GPU may have a parallel structure that is efficientfor parallel processing of large blocks of data, such as mathematicallyintensive data common to computer graphics applications, images, videos,etc.

Computer system 500 may also include a main or primary memory 508, suchas random access memory (RAM). Main memory 508 may include one or morelevels of cache. Main memory 508 may have stored therein control logic(i.e., computer software) and/or data.

Computer system 500 may also include one or more secondary storagedevices or memory 510. Secondary memory 510 may include, for example, ahard disk drive 512 and/or a removable storage device or drive 514.Removable storage drive 514 may be a floppy disk drive, a magnetic tapedrive, a compact disk drive, an optical storage device, tape backupdevice, and/or any other storage device/drive.

Removable storage drive 514 may interact with a removable storage unit518. Removable storage unit 518 may include a computer usable orreadable storage device having stored thereon computer software (controllogic) and/or data. Removable storage unit 518 may be a floppy disk,magnetic tape, compact disk, DVD, optical storage disk, and/ any othercomputer data storage device. Removable storage drive 514 may read fromand/or write to removable storage unit 518.

Secondary memory 510 may include other means, devices, components,instrumentalities or other approaches for allowing computer programsand/or other instructions and/or data to be accessed by computer system500. Such means, devices, components, instrumentalities or otherapproaches may include, for example, a removable storage unit 522 and aninterface 520. Examples of the removable storage unit 522 and theinterface 520 may include a program cartridge and cartridge interface(such as that found in video game devices), a removable memory chip(such as an EPROM or PROM) and associated socket, a memory stick and USBport, a memory card and associated memory card slot, and/or any otherremovable storage unit and associated interface.

Computer system 500 may further include a communication or networkinterface 524. Communication interface 524 may enable computer system500 to communicate and interact with any combination of externaldevices, external networks, external entities, etc. (individually andcollectively referenced by reference number 528). For example,communication interface 524 may allow computer system 500 to communicatewith external or remote devices 528 over communications path 526, whichmay be wired and/or wireless (or a combination thereof), and which mayinclude any combination of LANs, WANs, the Internet, etc. Control logicand/or data may be transmitted to and from computer system 500 viacommunication path 526.

Computer system 500 may also be any of a personal digital assistant(PDA), desktop workstation, laptop or notebook computer, netbook,tablet, smart phone, smart watch or other wearable, appliance, part ofthe Internet-of-Things, and/or embedded system, to name a fewnon-limiting examples, or any combination thereof.

Computer system 500 may be a client or server, accessing or hosting anyapplications and/or data through any delivery paradigm, including butnot limited to remote or distributed cloud computing solutions; local oron-premises software (“on-premise” cloud-based solutions); “as aservice” models (e.g., content as a service (CaaS), digital content as aservice (DCaaS), software as a service (SaaS), managed software as aservice (MSaaS), platform as a service (PaaS), desktop as a service(DaaS), framework as a service (FaaS), backend as a service (BaaS),mobile backend as a service (MBaaS), infrastructure as a service (IaaS),etc.); and/or a hybrid model including any combination of the foregoingexamples or other services or delivery paradigms.

Any applicable data structures, file formats, and schemas in computersystem 500 may be derived from standards including but not limited toJavaScript Object Notation (JSON), Extensible Markup Language (XML), YetAnother Markup Language (YAML), Extensible Hypertext Markup Language(XHTML), Wireless Markup Language (WML), MessagePack, XML User InterfaceLanguage (XUL), or any other functionally similar representations aloneor in combination. Alternatively, proprietary data structures, formatsor schemas may be used, either exclusively or in combination with knownor open standards.

In some embodiments, a tangible, non-transitory apparatus or article ofmanufacture comprising a tangible, non-transitory computer useable orreadable medium having control logic (software) stored thereon may alsobe referred to herein as a computer program product or program storagedevice. This includes, but is not limited to, computer system 500, mainmemory 508, secondary memory 510, and removable storage units 518 and522, as well as tangible articles of manufacture embodying anycombination of the foregoing. Such control logic, when executed by oneor more data processing devices (such as computer system 500), may causesuch data processing devices to operate as described herein.

Based on the teachings contained in this disclosure, it will be apparentto persons skilled in the relevant art(s) how to make and useembodiments of this disclosure using data processing devices, computersystems and/or computer architectures other than that shown in FIG. 5.In particular, embodiments can operate with software, hardware, and/oroperating system implementations other than those described herein.

It is to be appreciated that the Detailed Description section, and notany other section, is intended to be used to interpret the claims. Othersections can set forth one or more but not all exemplary embodiments ascontemplated by the inventor(s), and thus, are not intended to limitthis disclosure or the appended claims in any way.

While this disclosure describes exemplary embodiments for exemplaryfields and applications, it should be understood that the disclosure isnot limited thereto. Other embodiments and modifications thereto arepossible, and are within the scope and spirit of this disclosure. Forexample, and without limiting the generality of this paragraph,embodiments are not limited to the software, hardware, firmware, and/orentities illustrated in the figures and/or described herein. Further,embodiments (whether or not explicitly described herein) havesignificant utility to fields and applications beyond the examplesdescribed herein.

Embodiments have been described herein with the aid of functionalbuilding blocks illustrating the implementation of specified functionsand relationships thereof. The boundaries of these functional buildingblocks have been arbitrarily defined herein for the convenience of thedescription. Alternate boundaries can be defined as long as thespecified functions and relationships (or equivalents thereof) areappropriately performed. Also, alternative embodiments can performfunctional blocks, steps, operations, methods, etc. using orderingsdifferent than those described herein.

References herein to “one embodiment,” “an embodiment,” “an exampleembodiment,” or similar phrases, indicate that the embodiment describedcan include a particular feature, structure, or characteristic, butevery embodiment can not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it would be within the knowledge of persons skilled in therelevant art(s) to incorporate such feature, structure, orcharacteristic into other embodiments whether or not explicitlymentioned or described herein. Additionally, some embodiments can bedescribed using the expression “coupled” and “connected” along withtheir derivatives. These terms are not necessarily intended as synonymsfor each other. For example, some embodiments can be described using theterms “connected” and/or “coupled” to indicate that two or more elementsare in direct physical or electrical contact with each other. The term“coupled,” however, can also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other.

The breadth and scope of this disclosure should not be limited by any ofthe above-described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

1. (canceled)
 2. A computer implemented method, comprising: applying, byone or more computing devices, a regular expression search toenforcement information from an enforcement information source toidentify first citation data formatted according to a regular form forregulatory citations within the enforcement information; determining, bythe one or more computing devices, a classification for the enforcementinformation associated with the first citation data; classifying, by theone or more computing devices, a risk information document to determinea classification for the risk information document using phrase-basedscoring on the contents of the risk information document, wherein theclassification for the risk information document is associated withsecond citation data conforming to the regular form for regulatorycitations; comparing, by the one or more computing devices, the firstcitation data against the second citation data of the risk informationdocument to determine a correspondence between the classification forthe enforcement information and the classification for the riskinformation document; and storing, by the one or more computing devices,the enforcement information in association with the risk informationdocument, based on a match between the first citation data and thesecond citation data.
 3. The method of claim 2, wherein the enforcementinformation source comprises a database of a plurality of enforcementactions.
 4. The method of claim 2, wherein the enforcement informationsource comprises a database of a plurality of complaints.
 5. The methodof claim 2, further comprising: selecting, by the one or more computingdevices, a line of business from a plurality of lines of businesscorresponding to the classification for the risk information document;and notifying, by the one or more computing devices, the line ofbusiness of the association of the enforcement information with the riskinformation document.
 6. The method of claim 2, wherein classifying therisk information document using phrase-based scoring on the contents ofthe risk information document comprises: determining, by the one or morecomputing devices, the classification of the risk information documentby performing the phrase-based scoring against a plurality of riskinformation documents selected from a plurality of classifications. 7.The method of claim 2, further comprising: presenting, by the one ormore computing devices, in an interface, a plurality of discrepanciesincluding a discrepancy based on the association of the enforcementinformation with the risk information document; and permitting, by theone or more computing devices, arrangement of the plurality ofdiscrepancies on the interface according to a risk level.
 8. The methodof claim 2, further comprising: scraping, by the one or more computingdevices, the enforcement information from the enforcement informationsource.
 9. A system, comprising: a memory configured to storeoperations; and one or more processors configured to perform theoperations, the operations comprising: accessing enforcement informationfrom an enforcement information source; classifying the enforcementinformation to determine a classification for the enforcementinformation using phrase-based scoring on the contents of theenforcement information, wherein the classification for the enforcementinformation is associated with first citation data conforming to to aregular form for regulatory citations, classifying a risk informationdocument to determine a classification for the risk information documentusing phrase-based scoring on the contents of the risk informationdocument, wherein the classification for the risk information documentis associated with second citation data conforming to the regular formfor regulatory citations, comparing the first citation data of theenforcement information against the second citation data of the riskinformation document to determine a correspondence between theclassification for the enforcement information and the classificationfor the risk information document, and storing the enforcementinformation in association with the risk information document, based ona match between the first citation data and the second citation data.10. The system of claim 9, wherein the enforcement information sourcecomprises a database of a plurality of enforcement actions.
 11. Thesystem of claim 9, wherein the enforcement information source comprisesa database of a plurality of complaints.
 12. The system of claim 9, theoperations further comprising: selecting a line of business from aplurality of lines of business corresponding to the classification forthe risk information document; and notifying the line of business of theassociation of the enforcement information with the risk informationdocument.
 13. The system of claim 9, wherein classifying the riskinformation document using phrase-based scoring on the contents of therisk information document comprises: determining the classification ofthe risk information document by performing the phrase-based scoringagainst a plurality of risk information documents selected from aplurality of classifications.
 14. The system of claim 9, whereinclassifying the enforcement information using phrase-based scoring onthe contents of the enforcement information comprises: determining theclassification of the enforcement information by performing thephrase-based scoring against a plurality of enforcement informationdocuments selected from a plurality of classifications.
 15. The systemof claim 9, the operations further comprising: presenting, in aninterface, a plurality of discrepancies including a discrepancy based onthe association of the enforcement information with the risk informationdocument; and permitting arrangement of the plurality of discrepancieson the interface according to a risk level.
 16. A computer readablestorage device having instructions stored thereon, execution of which,by one or more processing devices, causes the one or more processingdevices to perform operations comprising: determining that a risk eventhas occurred based on the presence of enforcement information stored inassociation with a risk information document in a storage; andretrieving the enforcement information and the risk informationdocument, wherein the enforcement information is from an enforcementinformation source and is associated with the risk information documentbased on a match between first citation data of the enforcementinformation and second citation data of the risk information document todetermine a correspondence between a classification for the enforcementinformation and a classification for the risk information document,wherein the classification for the enforcement information is determinedusing phrase-based scoring on the contents of the enforcementinformation, the classification for the enforcement informationassociated with the first citation data conforming to a regular form forregulatory citations, and wherein the classification for the riskinformation document is determined using phrase-based scoring on thecontents of the risk information document, the classification for therisk information document associated with the second citation dataconforming to the regular form for regulatory citations.
 17. Thecomputer readable storage device of claim 16, wherein the enforcementinformation source comprises a database of a plurality of enforcementactions.
 18. The computer readable storage device of claim 16, theoperations further comprising: selecting a line of business from aplurality of lines of business corresponding to the classification forthe risk information document; and notifying the line of business of theassociation of the enforcement information with the risk informationdocument.
 19. The computer readable storage device of claim 16, whereinthe phrase-based scoring on the contents of the risk informationdocument comprises phrase-based scoring against a plurality of riskinformation documents selected from a plurality of classifications. 20.The computer readable storage device of claim 16, wherein thephrase-based scoring on the contents of the enforcement informationcomprises phrase-based scoring against a plurality of enforcementinformation documents selected from a plurality of classifications. 21.The computer readable storage device of claim 16, the operations furthercomprising: presenting, in an interface, a plurality of discrepanciesincluding a discrepancy based on the association of the enforcementinformation with the risk information document; and permittingarrangement of the plurality of discrepancies on the interface accordingto a risk level.